Hyperspectral (also known as “multispectral”) spectroscopy is an imaging technique that integrates multiples images of an object resolved at different spectral bands (i.e., ranges of wavelengths) into a single data structure, referred to as a three-dimensional hyperspectral data cube. Hyperspectral spectroscopy is often used to identify an individual component of a complex composition through the recognition of corresponding spectral signatures of the individual components in a particular hyperspectral data cube.
Hyperspectral spectroscopy has been used in a variety of applications, ranging from geological and agricultural surveying to military surveillance and industrial evaluation. Hyperspectral spectroscopy has also been used in medical applications to facilitate complex diagnosis and predict treatment outcomes. For example, medical hyperspectral imaging has been used to accurately predict viability and survival of tissue deprived of adequate perfusion, and to differentiate diseased (e.g. tumor) and ischemic tissue from normal tissue.
Some known oximetry algorithms that make use of Hyperspectral data operate by (i) irradiating the human tissue with narrow bands of light in the visible range, (ii) acquiring co-registered images of the tissue under the above illumination, (iii) calculating a reflectance signal from the tissue by normalizing the tissue reflectance signal by the reflectance from a highly reflective calibration standard, (iv) calculating the apparent absorption of tissue by taking the logarithm of the measured normalized reflectance signal, (v) calculating contribution of the absorption coefficient spectra of oxy-hemoglobin and deoxy-hemoglobin to the absorption spectrum of skin, and (vi) creating a map of weights for oxy- and deoxy-hemoglobin.
Such methods apply the so-called modified Beer-Lambert's (the MBL) law to analyze the reflectance image. The primary assumptions and limitations of this approach further include the limitation that tissue is modeled as homogeneous with depth. Then, nothing can be said about callus formation since this is primarily a change in the structure of the epidermis, the top layer of skin. A further limitation is that the tissue scattering coefficient is constant with wavelength. This assumption introduces little error for small wavelength ranges (e.g., the visible range). However, the accuracy of MBL diminishes if applied to a much larger range, say visible through near infrared.
Despite its limitations, such known oximetry techniques exhibit clinically relevant ulcer formation prediction capabilities. A description of such an algorithm is provided in Yudovsky and Durkin, 2011, “Spatial Frequency Domain Spectroscopy of Two Layer Media,” Journal of Biomedical Optics 16, 107005, (hereinafter “JBO-002”) which is hereby incorporated by reference herein in its entirety. In short, ulcer development prediction algorithms have been developed. They have been used to collect hyperspectral image data from the feet of multiple diabetic subjects exhibiting diabetic ulceration and at risk of forming new diabetic ulcers.
Over the course of one such study, a group of subjects with ulcers were observed to heal. A second group with ulcers was observed not to heal. Yet a third group was observed to develop new ulcers. Data from the first and second groups were used to develop an ulcer healing index described in Nouvong et al., “Evaluation of Diabetic Foot Ulcer Healing With Hyperspectral Imaging of Oxyhemoglobin and Deoxyhemoglobin,” Diabetes Care 32, 2056 (hereinafter “DC-001”) which is hereby incorporated by reference herein in its entirety. Data from the third group were used to develop the Formation Prediction Algorithm described in Yudovsky, 2011, “Assessing diabetic foot ulcer development risk with hyperspectral tissue oximetry,” Journal of Biomedical Optics 16, 026009 (hereinafter “JBO-001”), which is hereby incorporated by reference herein in its entirety.
The Formation Prediction Algorithm was calibrated by retrospectively analyzing the OXY and DEOXY signals as depicted in FIG. 8. Locations of formed ulcers were retrospectively analyzed for large changes in OXY/DEOXY signals. An area indicated by “T” was centered on the site known to become an ulcer. The average OXY and DEOXY values were calculated in that area. The same procedure was performed in all eight control regions (C1 through C8 in FIG. 8(b) around the test region. Then, the maximum difference was found between T and Ci for both OXY and DEOXY signals. The resulting maximum differences (MDs) are plotted for 21 subjects in FIG. 9 along with results from the same analysis performed at the contralateral site and 100 random locations on the feet of the diabetic subjects. Then, a threshold was drawn to separate ulcerating points in the first and third quadrants from other points. Then, any HSI image can be analyzed and points that fall within the “Affected tissue” areas highlighted in as indicated in FIG. 10.
Careful analysis of FIGS. 9 and 10 shows a significant number of false positive predictions. In fact, JBO-001 reported a sensitivity and specificity of 95% and 80%, respectively. The implications of these numbers are (i) 5% of affected tissue may not be detected and (ii) 20% of tissue identified as unaffected may in fact be healthy.
An attempt to improve these numbers of was made by developing a two layered model of human tissue as summarized in Yudowsky, 2010, “Two-Layer Optical Model of Skin for Early, Non-Invasive Detection of Wound Development on the Diabetic Foot,” Advanced Biomedical and Clinical Diagnostic Systems VIII, edited by Tuan Vo-Dinh, Warren S. Grundfest, Anita Mahadevan-Jansen, Proc. of SPIE Vol. 7555, 755514, 2010 SPIE (hereinafter “SPIE-001”), which in turn references Yudovsky, 2009, “Simple and accurate expressions for diffuse reflectance of semi-infinite and two-layer absorbing and scattering media,” Applied Optics 48, No. 35 (hereinafter “AO-001”) and Yudowsky, 2009, “Rapid and accurate estimation of blood saturation, melanin content, and epidermis thickness from spectral diffuse reflectance,” Applied Optics 49, 1707 (hereinafter “AO-002”), each of which is hereby incorporated by reference herein in its entirety. In short, a two layer model of light transfer through skin in the visible range (500 to 650 nm) was developed. The model calculated tissue reflectance as a function of melanin concentration, epidermal thickness, blood volume, saturation, and tissue scattering. AO-002 describes the application of the model developed in AO-001 to reflectance from human tissue. See also Yudovsky et al. 2011, “Assessing diabetic foot ulcer development risk with hyperspectral tissue oximetry,” Journal of Biomedical Optics 16, 026009-1 (hereinafter “JBP-001”), which is hereby incorporated by reference in its entirety.
Furthermore, Yudovsky et al., 2011, “Monitoring temporal development and healing of diabetic foot ulceration using hyperspectral imaging,” J. Biophotonics 4, No. 7-8, 565-576 (hereinafter “JBP-002”), which is hereby incorporated herein by reference in its entirety, shows the application of the model and method described in SPIE-001 to the temporal monitoring of ulcer development and healing. References 14 and 15 of JBP-002 explain that callus formation (epidermal thickening) are caused by excessive sheer pressure on the diabetic foot. Furthermore, callus formation exacerbates the pressure as the callus grows and thus leads to ulceration. FIGS. 4 and 5 of JBP-002 shows the average epidermal thickness on a preulcerative area on the feet of two diabetic subjects before, during, and after ulceration. FIG. 11 (adopted from FIG. 8 of JPB-002) shows epidermal thickness at an ulcer site and a control site before ulceration and during healing for one subject. It was observed that the epidermal thickness was larger on the affected area when compared with a control area on the other foot. Furthermore, the thickness increased from a baseline of 130 μm to 170 μm at the point of ulceration. Then, the skin thickness near the ulcer site decreased as the ulcer healed to near baseline.
JBP-002 showed the applicability of the model and method described in SPIE-001 to monitoring the temporal development of ulcers by detecting the formation of the thickening callus cap that forms above an ulcer. Furthermore, it showed that thinning of the callus around the ulcer as the ulcer heals. The development and use of the model described in AO-001 and AO-002 constitute an improvement over known algorithms that only provide oximetry information. Many more parameters can be detected from tissue reflectance in the visible range. The analysis described in JBP-002 establishes the possibility that HSI—with proper modeling—can be used to not only predict ulcer formation but also provide an estimate on the speed of ulcer formation by measuring the growth and receding of the callus cap.
Some known tissue oximetry algorithms are based on a homogeneous MBL law model of light transfer through skin in the visible range (500 to 650 nm). The explicit limitations of such models and choice of interrogation wavelength include that they are limited to detection of oxy and deoxy hemoglobin in the superficial tissue of skin. Visible light does not penetrate more than 1 millimeter. Further, such know methods cannot detect melanin concentration because pigmentation is treated as nuisance parameters and calibrated away. Further still, they cannot be extended to beyond the visible range because (i) it is calibrated for visible light and (ii) it is derived with the assumption of static scattering coefficient. This implies that detection of water content (hydration) is not feasible with such algorithms without major modification. Further still, such algorithms cannot be extended to detect epidermal thickness since this requires an explicit treatment of light transfer through the two layers of skin. Also, they are typically calibrated specifically to human tissue on the arms and feet. It is thus difficult to apply the same technology to other external and internal organs. Moreover, callus completely occludes the dermal signal in the visible range thus making oximetry over thick calluses unsatisfactory in the visible range. In such approaches, ulcer prediction is based on the OXY and DEOXY values and so the determination of healthy/non-healthy tissue in FIG. 9 is determined by a linear classifier with two parameters. Further, the output of such known algorithms, as exemplified in FIG. 10, shows Boolean classification. In fact, a tissue area may have a likelihood of ulceration, with some areas more likely to ulcerate than others. Further still, the output of such algorithms, as exemplified in FIG. 10, do not give a sense of time to ulceration. Presented with FIG. 10, a clinician has no sense of likelihood of ulcer formation one, two three, etc. weeks after imaging.
The model presented in SPIE-001 and applications presented in JBP-002 represent improvements in the field. However, the model presented in SPIE-001 has limitations. For instance, the model accurately predicts light transfer in the visible range (500 to 600 nm). The accuracy of the model diminishes below the useful threshold beyond 600 nm since human skin becomes highly scattering (see FIGS. 9 and 10 in AO-001). Further, the two layer model presented in AO-001 exhibits an error in predicting reflectance from two layer media of up to 8%. Additionally, the error in predicting the reflectance depends on the range of input optical and geometric values. A sensor with offset/noise that is proportional to the measured quantity is undesirable in medical applications where accuracy is essential. The careful reviewer will see that the model presented in AO-001 and AO-002 shows feasibility but does not constitute a production solution. The inverse method presented in AO-002 is based on the forward model presented in AO-001. Measured reflectance is compared to output from the modeled reflectance and then the input parameters to the model are iteratively modified until the modeled and measured reflectance are identical within a tolerable error. The iteration typically converges after 100 attempts. An HSI may contain on the order of 100,000 pixels. Thus, calculation speed is important.
The model presented in AO-001 is computationally intense. For example, the images in JBP-002 took many hours to produce on a desktop. An industrial implementation of JBP-002 would likely require highly paralyzed embedded devices such as a DSP or GPU. Such devices are efficient at processing simple functions with few steps. Thus, the AO-001 model would be cumbersome to implement on a single digital signal processor or graphic processor unit because it is complex and requires multiple look up tables. It is thus difficult to productize.
There are limitations of the results presented in JBP-002 with regard to temporal analysis of callus formation and receding. Callus on the healthy or diabetic foot can be millimeters thick. However, epidermal thickness presented in JBP-002 ranges between 0 and 150 μm (+150 μm tests the accuracy of the model presented in AO-001). It is likely that the epidermal thickness presented in JBP-002 is sensitive to true epidermal thickness but may not have a linear relationship to the physical quantity. The range of wavelengths used by typical oximetry algorithms that are limited to visible light spectrums may also affect the estimation of epidermal thickness. Epidermal tissue is highly absorbing in the visible range. Thus an epidermis of 150 μm and 1500 μm will have essentially the same reflectance in the visible range since light does not penetrate the callus to the deeper layers. In both cases, the callus will look like a semi-infinite (one layer) and no useful information about the dermis and the true epidermal thickness can be determined. NIR light can penetrate a thick callus. The combination of visible and NIR reflectance provides a complete view of the tissue. The visible light primarily interrogates the epidermis while the NIR light primarily interrogates the dermis. It is like looking at a wall and then looking through the wall at the content behind. A complete picture of the entire building can be reported. Clinically, temporal data from only two patients was analyzed. This is not enough to develop a predictive index that also gives a time-to-ulceration or speed of ulceration score.
Given the above background, what is needed in the art are improvement methods for performing tissue oximetry.